There is an increasing need for recognition of handwriting. Portable data handling devices, such as PDAs (Personal Digital Assistant), mobile phones and portable computers, are becoming increasingly popular. In order to enter data into these portable devices, a text insertion unit is needed.
Text insertion units have formerly been implemented as keyboards. This, however, sets limits to the minimum size of the portable device, and therefore a different way of inserting text into the device is needed in order to enable smaller devices.
In such devices, the keyboards can now be replaced by some kind of recognition of handwritten text. A common solution is to arrange a pressure-sensitive area where a user can write characters. The characters are then interpreted and handled by the portable device. It is, of course, also of interest to replace keyboards of regular stationary computers.
Thus, recognition of handwritten characters is an important task in portable and stationary devices. In recognition of a handwritten text, each character is often recognized individually. However, this implies that a lot of information, which could be useful for a correct. recognition, is disregarded. For example, the height of a character compared to other characters is useful when trying to distinguish between ‘o’ and ‘O’. Also, some characters extend below or above other characters. This could be used for recognition of a ‘P’ compared to a ‘p’.
There are some ways today of using the information given by characters that extend below or above other characters for better recognition of handwritten text. These methods try to define a base line and a core region to decide where the characters should be positioned. The base line is the line on which the text stands, but characters as g and p extend below it. The core region is the region over the base line, which all characters intersect. Characters, such as ‘a’ and ‘m’, are completely inside the core region, whereas ‘l’ and ‘b’ extend above the core region. However, it could be hard to find a base line for the text and it is hard to define the core region, especially if the characters are inclined. Furthermore, this method is specialized in Latin characters and could not be used in e.g. Chinese characters.
Another method is to have reference patterns for two or more characters combined and recognize each pattern as a whole. However, this implies that a very large number of reference patterns are needed and thus the recognition becomes slow.